I don't have any experience with MongoDB, but just gave my 2 cents here.
Your code is not efficient, as using the "+=" on String, and you could have reused the Text
object in your mapper, as it is a mutable class, to be reused and avoid creating it again
and again like "new Text()" in the mapper. My guess that BSONWritable should be a similar
mutable class, if it aims to be used like the rest Writable Hadoop class.
But even like that, it should just make your mapper run slower, as a lot of objects need to
be GC, instead of OOM.
When you claim 96G ram, I am not sure what do you mean? From what you said, it failed in
mapper stage, so let's focus on mapper. What max heap size you gave to the mapper task? I
don't think 96G is the setting you mean to give to each mapper task. Otherwise, the only place
I can think is that there are millions of Strings to be appended in one record by "+=" and
cause the OOM.
You need to answer the following questions by yourself:
1) Are there any mappers successful?2) The OOM mapper, is it always on the same block? If
so, you need to dig into the source data for that block, to think why it will cause OOM.3)
Did you give reasonable heap size for the mapper? What it is?
Yong
From: Blanca.Hernandez@willhaben.at
To: user@hadoop.apache.org
Subject: Extremely amount of memory and DB connections by MR Job
Date: Mon, 29 Sep 2014 12:57:41 +0000
Hi,
I am using a hadoop map reduce job + mongoDb.
It goes against a data base 252Gb big. During the job the amount of conexions is over 8000
and we gave already 9Gb RAM. The job is still crashing because of a OutOfMemory with only
a 8% of the mapping done.
Are this numbers normal? Or did we miss something regarding configuration?
I attach my code, just in case the problem is with it.
Mapper:
public class AveragePriceMapper extends Mapper<Object, BasicDBObject, Text, BSONWritable>
{
@Override
public void map(final Object key, final BasicDBObject val, final Context context) throws
IOException, InterruptedException {
String id = "";
for(String propertyId : currentId.split(AveragePriceGlobal.SEPARATOR)){
id += val.get(propertyId) + AveragePriceGlobal.SEPARATOR;
}
BSONWritable bsonWritable = new BSONWritable(val);
context.write(new Text(id), bsonWritable);
}
}
Reducer:
public class AveragePriceReducer extends Reducer<Text, BSONWritable, Text, Text> {
public void reduce(final Text pKey, final Iterable<BSONWritable> pValues, final
Context pContext) throws IOException, InterruptedException {
while(pValues.iterator().hasNext() && continueLoop){
BSONWritable next = pValues.iterator().next();
//Make some calculations
} pContext.write(new Text(currentId), new Text(new MyClass(currentId, AveragePriceGlobal.COMMENT,
0, 0).toString()));
}
}
The configuration includes a query which filters the number of objects to analyze (not the
252Gb will be analyzed).
Many thanks. Best regards,
Blanca